MulTINCo: Multilingual Traditional Immersion and Native Corpus. Better-documented multi-literacy practices for more refined SLA studies

Whilst the links between learner corpus research (LCR) and Second Language Acquisition (SLA) have long been debated, McEnery et al. (2019, 17) claim that the promise made by Granger in 2009 that learner corpora would “soon be accepted as a bona fide data type in SLA research” has yet to be realized. This article aims to go one way towards realizing this promise by illustrating the benefits of collecting and analyzing data sets that better document multiliteracy practices. We first contextualize our work within the field of LCR where calls for more multidimensional data sets have been made. We then present a new database called MulTINCo – Multilingual Traditional, Immersion, and Native Corpus – collected within the context of a large-scale project on Content and Language Integrated Learning (CLIL) in French-speaking Belgium. As our data set contains rich metadata and blends corpus data with other data types, we explain how it can help bridge the LCR/SLA gap. In Sections 3 and 4, we describe the data collected and the interface. In the last section of the paper, we wrap up the discussion on the methodological assets of such multidimensional data sets for SLA studies, and present outlooks on future research.